1. 初步(8 课时):条件期望;离散时间鞅; Doob 可选抽样定理; 上、下鞅; 鞅不等式;鞅收敛定理;连续时
间鞅等。
Preliminaries (8 hours): Conditional Expectation; Discrete time Martingale; Doob’s Optional Sampling
Theorems; Supermartingales and Submartingales; Martingale Inequalities; Martingale Convergence
Theorems, Introduction to Continuous Time Martingales, etc.
2. 布朗运动(8 课时):布朗运动定义、分布,流;鞅性质;二次变差;马尔科夫性;首中时;反射原理
等。
Brownian Motion(BM) (8 hours): Definition of BM; Distribution of BM; Filtration for BM; Martingale
Property for BM; Quadratic Variation; Markov Property of BM; First Passage Time Distributions;
Reflection Principle, etc.
3. Itô 积分(16 课时):Itô 积分定义;Itô-Doeblin 公式;分部积分;随机 Fubini 定理;Girsanov 定理;布朗
鞅与表示定理;Feynman-Kac 表示等。
Itô Integrals (16 hours): Definition of Itô’s Integral; Itô-Doeblin Formula; Integration by parts; Stochastic
Fubini Theorem; The Girsanov Theorem; The Brownian Martingale Representation Theorem; The
Feynman-Kac Representation, etc.
4. 随机微分方程理论(8 课时)
Stochastic Differential Equations (8 hours)
5. 倒向随机微分方程(8 课时)
Backward Stochastic Differential Equations (8 hours)
1. J. Xiong. An Introduction to Stochastic Filtering Theory. Oxford Graduate Texts in Mathematics,
18. Oxford University Press, 2008.
2. Jiongmin Yong and Xunyu Zhou, Stochastic Control: Hamiltonian Systems and HJB Equations